Predicting the Number of Fibular Segments to Reconstruct Mandibular Defects
Bibliographic record
Abstract
OBJECTIVES: Several classification schemes have been proposed to categorize mandibular defects following surgical resection; however, there is a paucity of data to guide an optimal reconstruction. This study examines the feasibility of using a geometric algorithm to simplify and determine the optimal reconstruction for a given mandibular defect. This algorithm is then applied to three different mandible defect classification schemes to correlate the defect type and number of bony segments required for reconstruction. METHODS: Computed tomography (CT) scans of 48 mandibles were decomposed into curvilinear representations and analyzed using the Ramer-Douglas-Peucker algorithm. In total, 720 mandibular defects were created and subsequently analyzed utilizing three commonly referenced classification systems. For each defect, the number of bony segments required to reconstruct each defect was computed. RESULTS: A wide variance in the number of segments needed for optimal reconstruction was observed across existing classifications. A six-segment total mandible reconstruction best reconstituted mandibular form in all 48 mandibles. CONCLUSION: Defect classification schemes are not adaptable to predicting the number of fibula segments required for a given defect. Additionally, cephalometric templates may not be applicable in all clinical settings. The Ramer-Douglas-Peucker algorithm is well suited for providing case-specific predictions of reconstruction plans in a reproducible manner. LEVEL OF EVIDENCE: IV Laryngoscope, 130:E619-E624, 2020.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".